Polar and subpolar regions are fragile environments, where the introduction of alien species may completely change ecosystem dynamics if the alien species become keystone species (e.g. Croll, 2005). The increasing number of human visits, together with climate change, are favouring the introduction and settling of new invaders to these regions, particularly in Antarctica (Hughes et al. 2015). Within this context, the joint use of Species Distribution Models (SDM) –to assess the areas potentially suitable for the aliens– with other measures of the potential to become successful invaders can inform on the need for devoting specific efforts to eradicate these new species before they become naturalized (e.g. Pertierra et al. 2016).
Bazzichetto et al. (2020) use data from a detailed inventory, SDMs and trait data altogether to assess the drivers of invasion success of six alien plants on Possession Island, in the remote sub-Antarctic archipelago of Crozet. SDMs have inherent limitations to describe different aspects of species distributions, including the fundamental niche and, with it, the areas that could host viable populations (Hortal et al. 2012). Therefore, their utility to predict future biological invasions is limited (Jiménez-Valverde et al. 2011). However, they can be powerful tools to describe species range dynamics if they are thoughtfully used by adopting conscious decisions about the techniques and data used, and interpreting carefully the actual implications of their results.
This is what Bazzichetto et al. (2020) do, using General Linear Models (GLM) –a technique well rooted in the original niche-based SDM theory (e.g. Austin 1990)– that can provide a meaningful description of the realized niche within the limits of an adequately sampled region. Further, as alien species share and are similarly affected by several steps of the invasion process (Richardson et al. 2000), these authors model the realized distribution of the six species altogether. This can be done through the recently developed joint-SDM, a group of techniques where the co-occurrence of the modelled species is explicitly taken into account during modelling (e.g. Pollock et al. 2014). Here, the addition of species traits has been identified as a key step to understand the associations of species in space (see Dormann et al. 2018). Bazzichetto et al. (2020) combine their GLM-based SDM for each species with a so-called multi-SDM approach, where they assess together the consistency in the interactions between both species and topographically-driven climate variations, and several plant traits and two key anthropic factors –accessibility from human settlements and distance to hiking paths.
This work is a good example on how a theoretically meaningful SDM approach can provide useful –though perhaps not deep– insights on biological invasions for remote landscapes threatened by biotic homogenization. By combining climate and topographic variables as proxies for the spatial variations in the abiotic conditions regulating plant growth, measures of accessibility, and traits of the plant invaders, Bazzichetto et al. (2020) are able to identify the different effects that the interactions between the potential intensity of propagule dissemination by humans, and the ecological characteristics of the invaders themselves, may have on their invasion success.
The innovation of modelling together species responses is important because it allows dissecting the spatial dynamics of spread of the invaders, which indeed vary according to a handful of their traits. For example, their results show that no all old residents have profited from the larger time of residence in the island, as Poa pratensis is seemingly as dependent of a higher intensity of human activity as the newcomer invaders in general are. According to Bazzichetto et al. trait-based analyses, these differences are apparently related with plant height, as smaller plants disperse more easily. Further, being perennial also provides an advantage for the persistence in areas with less human influence. This puts name, shame and fame to the known influence of plant life history on their dispersal success (Beckman et al. 2018), at least for the particular case of plant invasions in Possession Island.
Of course this approach has limitations, as data on the texture, chemistry and temperature of the soil are not available, and thus were not considered in the analyses. These factors may be critical for both establishment and persistence of small plants in the harsh Antarctic environments, as Bazzichetto et al. (2020) recognize. But all in all, their results provide key insights on which traits may confer alien plants with a higher likelihood of becoming successful invaders in the fragile Antarctic and sub-Antarctic ecosystems. This opens a way for rapid assessments of invasibility, which will help identifying which species in the process of naturalizing may require active contention measures to prevent them from becoming ecological game changers and cause disastrous cascade effects that shift the dynamics of native ecosystems.

References

Austin, M. P., Nicholls, A. O., and Margules, C. R. (1990). Measurement of the realized qualitative niche: environmental niches of five Eucalyptus species. Ecological Monographs, 60(2), 161-177. doi: https://doi.org/10.2307/1943043
Bazzichetto, M., Massol, F., Carboni, M., Lenoir, J., Lembrechts, J. J. and Joly, R. (2020) Once upon a time in the far south: Influence of local drivers and functional traits on plant invasion in the harsh sub-Antarctic islands. bioRxiv, 2020.07.19.210880, ver. 3 peer-reviewed and recommended by PCI Ecology. doi: https://doi.org/10.1101/2020.07.19.210880
Beckman, N. G., Bullock, J. M., and Salguero-Gómez, R. (2018). High dispersal ability is related to fast life-history strategies. Journal of Ecology, 106(4), 1349-1362. doi: https://doi.org/10.1111/1365-2745.12989
Croll, D. A., Maron, J. L., Estes, J. A., Danner, E. M., and Byrd, G. V. (2005). Introduced predators transform subarctic islands from grassland to tundra. Science, 307(5717), 1959-1961. doi: https://doi.org/10.1126/science.1108485
Dormann, C. F., Bobrowski, M., Dehling, D. M., Harris, D. J., Hartig, F., Lischke, H., Moretti, M. D., Pagel, J., Pinkert, S., Schleuning, M., Schmidt, S. I., Sheppard, C. S., Steinbauer, M. J., Zeuss, D., and Kraan, C. (2018). Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions. Global Ecology and Biogeography, 27(9), 1004-1016. doi: https://doi.org/10.1111/geb.12759
Jiménez-Valverde, A., Peterson, A., Soberón, J., Overton, J., Aragón, P., and Lobo, J. (2011). Use of niche models in invasive species risk assessments. Biological Invasions, 13(12), 2785-2797. doi: https://doi.org/10.1007/s10530-011-9963-4
Hortal, J., Lobo, J. M., and Jiménez-Valverde, A. (2012). Basic questions in biogeography and the (lack of) simplicity of species distributions: Putting species distribution models in the right place. Natureza & Conservação – Brazilian Journal of Nature Conservation, 10(2), 108-118. doi: https://doi.org/10.4322/natcon.2012.029
Hughes, K. A., Pertierra, L. R., Molina-Montenegro, M. A., and Convey, P. (2015). Biological invasions in terrestrial Antarctica: what is the current status and can we respond? Biodiversity and Conservation, 24(5), 1031-1055. doi: https://doi.org/10.1007/s10531-015-0896-6
Pertierra, L. R., Baker, M., Howard, C., Vega, G. C., Olalla-Tarraga, M. A., and Scott, J. (2016). Assessing the invasive risk of two non-native Agrostis species on sub-Antarctic Macquarie Island. Polar Biology, 39(12), 2361-2371. doi: https://doi.org/10.1007/s00300-016-1912-3
Pollock, L. J., Tingley, R., Morris, W. K., Golding, N., O'Hara, R. B., Parris, K. M., Vesk, P. A., and McCarthy, M. A. (2014). Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods in Ecology and Evolution, 5(5), 397-406. doi: https://doi.org/10.1111/2041-210X.12180
Richardson, D. M., Pyšek, P., Rejmánek, M., Barbour, M. G., Panetta, F. D., and West, C. J. (2000). Naturalization and invasion of alien plants: concepts and definitions. Diversity and Distributions, 6(2), 93-107. doi: https://doi.org/10.1046/j.1472-4642.2000.00083.x

Ecology is the study of the distribution of organisms in space and time and their interactions. As such, there is a tradition of studies relating abiotic environmental conditions to species distribution, while another one is concerned by the effects of consumers on the abundance of their resources.  Interestingly, joining the dots appears more difficult than it would suggest: eluding the effect of species interactions on distribution remains one of the greatest challenges to elucidate nowadays (Kissling et al. 2012). Theory suggests that yes, species interactions such as predation and competition should influence range limits (Godsoe et al. 2017), but the common intuition among many biogeographers remains that over large areas such as regions and continents, environmental drivers like temperature and precipitation overwhelm their local effects. Answering this question is of primary importance in the context where species are moving around with climate warming.  Inconsistencies in food web structure may arise with asynchronized movements of consumers and their resources, leading to a major disruption in regulation and potentially ecosystem functioning. Solving this problem, however, remains very challenging because we have to rely on observational data since experiments are hard to perform at the biogeographical scale. 

The study of St-Gelais is an interesting step forward to solve this problem. Their main objective was to assess the strength of the association between phytoplankton and zooplankton communities at a large spatial scale, looking at the spatial covariation of both taxonomic and functional composition. To do so, they undertook a massive survey of more than 100 lakes across three regions of the boreal region of Québec. Species and functional composition were recorded, along with a set of abiotic variables. Classic community ecology at this point. The difficulty they faced was to disentangle the multiple causal relationships involved in the distribution of both trophic levels. Teasing apart bottom-up and top-down forces driving the assembly of plankton communities using observational data is not an easy task. On the one hand, both trophic levels could respond to variations in temperature, nutrient availability and dissolved organic carbon. The interpretation is fairly straightforward if the two levels respond to different factors, but the situation is much more complicated when they do respond similarly. There are potentially three possible underlying scenarios. First, the phyto and zooplankton communities may share the same environmental requirements, thereby generating a joint distribution over gradients such as temperature and nutrient availability. Second, the abiotic environment could drive the distribution of the phytoplankton community, which would then propagate up and influence the distribution of the zooplankton community. Alternatively, the abiotic environment could constrain the distribution of the zooplankton, which could then affect the one of phytoplankton. In addition to all of these factors, St-Gelais et al also consider that dispersal may limit the distribution, well aware of previous studies documenting stronger dispersal limitations for zooplankton communities. 

Unfortunately, there is not a single statistical approach that could be taken from the shelf and used to elucidate drivers of co-distribution. Joint species distribution was once envisioned as a major step forward in this direction (Warton et al. 2015), but there are several limits preventing the direct interpretation that co-occurrence is linked to interactions (Blanchet et al. 2020). Rather, St-Gelais used a variety of multivariate statistics to reveal the structure in their observational data. First, using a Procrustes analysis (a method testing if the spatial variation of one community is correlated to the structure of another community), they found a significant correlation between phytoplankton and zooplankton communities, indicating a taxonomic coupling between the groups. Interestingly, this observation was maintained for functional composition only when interaction-related traits were considered. At this point, these results strongly suggest that interactions are involved in the correlation, but it's hard to decipher between bottom-up and top-down perspectives. A complementary analysis performed with a constrained ordination, per trophic level, provided complementary pieces of information. First observation was that only functional variation was found to be related to the different environmental variables, not taxonomic variation. Despite that trophic levels responded to water quality variables, spatial autocorrelation was more important for zooplankton communities and the two layers appear to respond to different variables. 

It is impossible with those results to formulate a strong conclusion about whether grazing influence the co-distribution of phytoplankton and zooplankton communities. That's the mere nature of observational data. While there is a strong spatial association between them, there are also diverging responses to the different environmental variables considered. But the contrast between taxonomic and functional composition is nonetheless informative and it seems that beyond the idiosyncrasies of species composition, trait distribution may be more informative and general. Perhaps the most original contribution of this study is the hierarchical approach to analyze the data, combined with the simultaneous analysis of taxonomic and functional distributions. Having access to a vast catalog of multivariate statistical techniques, a careful selection of analyses helps revealing key features in the data, rejecting some hypotheses and accepting others. Hopefully, we will see more and more of such multi-trophic approaches to distribution because it is now clear that the factors driving distribution are much more complicated than anticipated in more traditional analyses of community data. Biodiversity is more than a species list, it is also all of the interactions between them, influencing their distribution and abundance (Jordano 2016).

References

Blanchet FG, Cazelles K, Gravel D (2020) Co-occurrence is not evidence of ecological interactions. Ecology Letters, 23, 1050–1063. https://doi.org/10.1111/ele.13525

Godsoe W, Jankowski J, Holt RD, Gravel D (2017) Integrating Biogeography with Contemporary Niche Theory. Trends in Ecology & Evolution, 32, 488–499. https://doi.org/10.1016/j.tree.2017.03.008

Jordano P (2016) Chasing Ecological Interactions. PLOS Biology, 14, e1002559. https://doi.org/10.1371/journal.pbio.1002559

Kissling WD, Dormann CF, Groeneveld J, Hickler T, Kühn I, McInerny GJ, Montoya JM, Römermann C, Schiffers K, Schurr FM, Singer A, Svenning J-C, Zimmermann NE, O’Hara RB (2012) Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents. Journal of Biogeography, 39, 2163–2178. https://doi.org/10.1111/j.1365-2699.2011.02663.x

St-Gelais NF, Vogt RJ, Giorgio PA del, Beisner BE (2021) The taxonomic and functional biogeographies of phytoplankton and zooplankton communities across boreal lakes. bioRxiv, 373332, ver. 4 peer-reviewed and recommended by Peer community in Ecology. https://doi.org/10.1101/373332

Warton DI, Blanchet FG, O’Hara RB, Ovaskainen O, Taskinen S, Walker SC, Hui FKC (2015) So Many Variables: Joint Modeling in Community Ecology. Trends in Ecology & Evolution, 30, 766–779. https://doi.org/10.1016/j.tree.2015.09.007

Wisz MS, Pottier J, Kissling WD, Pellissier L, Lenoir J, Damgaard CF, Dormann CF, Forchhammer MC, Grytnes J-A, Guisan A, Heikkinen RK, Høye TT, Kühn I, Luoto M, Maiorano L, Nilsson M-C, Normand S, Öckinger E, Schmidt NM, Termansen M, Timmermann A, Wardle DA, Aastrup P, Svenning J-C (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biological Reviews, 88, 15–30. https://doi.org/10.1111/j.1469-185X.2012.00235.x

To observe, characterise, identify, understand, predict... This is the approach that researchers follow every day. This sequence is tirelessly repeated as the biological model, the targeted ecosystem and/or the experimental, environmental or modelling conditions change. This way of proceeding is essential in a world of rapid change in response to the frenetic pace of intensifying pressures and forcings that impact ecosystems. To better understand our Earth and the dynamics of its components, to map ecosystems and diversity patterns, and to identify changes, humanity had to demonstrate inventiveness and defy gravity. 

Gustave Hermite and Georges Besançon were the first to launch aloft balloons equipped with radio transmitters, making possible the transmission of meteorological data to observers in real time [1]. The development of aviation in the middle of the 20th century constituted a real leap forward for the frequent acquisition of aerial observations, leading to a significant improvement in weather forecasting models. The need for systematic collection of data as holistic as possible – an essential component for the observation of complex biological systems - has resulted in pushing the limits of technological prowess. 

The conquest of space and the concurrent development of satellite observations has largely contributed to the collection of a considerable mass of data, placing our Earth under the "macroscope" - a concept introduced to ecology in the early 1970s by Howard T. Odum (see [2]), and therefore allowing researchers to move towards a better understanding of ecological systems, deterministic and stochastic patterns … with the ultimate goal of improving management actions [2,3]. Satellite observations have been carried out for nearly five decades now [3] and have greatly contributed to a better qualitative and quantitative understanding of the functioning of our planet, its diversity, its climate... and to a better anticipation of possible future changes (e.g., [4-7]).

This access to rich and complex sources of information, for which both spatial and temporal resolutions are increasingly fine, results in the implementation of increasingly complex computation-based analyses, in order to meet the need for a better understanding of ecological mechanisms and processes, and their possible changes. Steven Levitt stated that "Data is one of the most powerful mechanisms for telling stories". This is so true … Data should not be used as a guide to thinking and a critical judgment at each stage of the data exploitation process should not be neglected. 

This is what Mahood et al. [8] rightly remind us in their article "Ten simple rules for working with high-resolution remote sensing data" in which they provide the fundamentals to consider when working with data of this nature, a still underutilized resource in several topics, such as conservation biology [3]. In this unconventional article, presented in a pedagogical way, the authors remind different generations of readers how satellite data should be handled and processed. The authors aim to make the readers aware of the most frequent pitfalls encouraging them to use data adapted to their original question, the most suitable tools/methods/procedures, to avoid methodological overkill, and to ensure both ethical use of data and transparency in the research process. While access to high-resolution data is increasingly easy thanks to the implementation of dedicated platforms [4], and because of the development of easy-to-use processing software and pipelines, it is important to take the time to recall some of the essential rules and guidelines for managing them, from new users with little or no experience who will find in this article the recommendations, resources and advice necessary to start exploiting remote sensing data, to more experienced researchers.

References

[1] Jeannet P, Philipona R, and Richner H (2016). 8 Swiss upper-air balloon soundings since 1902. In: Willemse S, Furger M (2016) From weather observations to atmospheric and climate sciences in Switzerland: Celebrating 100 years of the Swiss Society for Meteorology. vdf Hochschulverlag AG. 

[2] Odum HT (2007) Environment, Power, and Society for the Twenty-First Century: The Hierarchy of Energy. Columbia University Press.

[3] Boyle SA, Kennedy CM, Torres J, Colman K, Pérez-Estigarribia PE, Sancha NU de la (2014) High-Resolution Satellite Imagery Is an Important yet Underutilized Resource in Conservation Biology. PLOS ONE, 9, e86908. https://doi.org/10.1371/journal.pone.0086908

[4] Le Traon P-Y, Antoine D, Bentamy A, Bonekamp H, Breivik LA, Chapron B, Corlett G, Dibarboure G, DiGiacomo P, Donlon C, Faugère Y, Font J, Girard-Ardhuin F, Gohin F, Johannessen JA, Kamachi M, Lagerloef G, Lambin J, Larnicol G, Le Borgne P, Leuliette E, Lindstrom E, Martin MJ, Maturi E, Miller L, Mingsen L, Morrow R, Reul N, Rio MH, Roquet H, Santoleri R, Wilkin J (2015) Use of satellite observations for operational oceanography: recent achievements and future prospects. Journal of Operational Oceanography, 8, s12–s27. https://doi.org/10.1080/1755876X.2015.1022050

[5] Turner W, Rondinini C, Pettorelli N, Mora B, Leidner AK, Szantoi Z, Buchanan G, Dech S, Dwyer J, Herold M, Koh LP, Leimgruber P, Taubenboeck H, Wegmann M, Wikelski M, Woodcock C (2015) Free and open-access satellite data are key to biodiversity conservation. Biological Conservation, 182, 173–176. https://doi.org/10.1016/j.biocon.2014.11.048

[6] Melet A, Teatini P, Le Cozannet G, Jamet C, Conversi A, Benveniste J, Almar R (2020) Earth Observations for Monitoring Marine Coastal Hazards and Their Drivers. Surveys in Geophysics, 41, 1489–1534. https://doi.org/10.1007/s10712-020-09594-5

[7] Zhao Q, Yu L, Du Z, Peng D, Hao P, Zhang Y, Gong P (2022) An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends. Remote Sensing, 14, 1863. https://doi.org/10.3390/rs14081863

[8] Mahood AL, Joseph MB, Spiers A, Koontz MJ, Ilangakoon N, Solvik K, Quarderer N, McGlinchy J, Scholl V, Denis LS, Nagy C, Braswell A, Rossi MW, Herwehe L, Wasser L, Cattau ME, Iglesias V, Yao F, Leyk S, Balch J (2021) Ten simple rules for working with high resolution remote sensing data. OSFpreprints, ver. 6 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.31219/osf.io/kehqz

Estimating populations size of widespread, common species in a relatively large and heterogeneous country like France is difficult for several reasons, from having a sample covering well the diverse ecological gradients to accounting for detectability, the fact that absence of a species may represent a false negative, the species being present but not detected. Bird communities have been the focus of a very large number of studies, with some countries like the UK having long traditions of monitoring both common and rare species. Nabias et al. use a large, structured citizen science project to provide new estimates of common bird species, accounting for detectability and using different habitat and climate covariates to extrapolate abundance to non-sampled areas. About 2/3 of the species had estimates higher than what would have been expected using a previous attempt at estimating population size based in part on expert knowledge and projected using estimates of trends to the period covered by the citizen science sampling. Some species showed large differences between the two estimates, which could be in part explained by accounting for detectability.

This paper uses what is called model-based inference (as opposed to design-based inference, that uses the design to make inferences about the whole population; Buckland et al. 2000), both in terms of detectability and habitat suitability. The estimates obtained depend on how well the model components approximate the underlying processes, which in a complex dataset like this one is not easy to assess. But it clearly shows that detectability may have substantial implications for the population size estimates. This is of course not new but has rarely been done at this scale and using a large sample obtained on many species. Interesting further work could focus on testing the robustness of the model-based approach by for example sampling new plots and compare the expected values to the observed values. Such a sampling could be stratified to maximize the discrimination between expected low and high abundances, at least for species where the estimates might be considered as uncertain, or for which estimating population sizes is deemed important.

References

Buckland, S. T., Goudie, I. B. J., & Borchers, D. L. (2000). Wildlife Population Assessment: Past Developments and Future Directions. Biometrics, 56(1), 1-12. https://doi.org/10.1111/j.0006-341X.2000.00001.x

 Nabias, J., Barbaro, L., Fontaine, B., Dupuy, J., Couzi, L., et al. (2024) Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability. HAL, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://hal.science/hal-04478371

Invasive plant species are widely studied by the ecologist community, especially in wetlands. Indeed, alien plants are considered one of the major threats to wetland biodiversity (Reid et al., 2019). Ludwigia grandiflora subsp. hexapetala (Hook. & Arn.) G.L.Nesom & Kartesz, 2000 (Lgh) is one of them and has received particular attention for a long time (Hieda et al., 2020; Thouvenot, Haury, & Thiebaut, 2013). The ecology of this invasive species and its effect on its biotic and abiotic environment has been studied in previous works. Different processes were demonstrated to explain their invasibility such as allelopathic interference (Dandelot et al., 2008), resource competition (Gérard et al., 2014), and high phenotypic plasticity (Thouvenot, Haury, & Thiébaut, 2013), to cite a few of them. However, although vegetative reproduction is a well-known invasive process for alien plants like Lgh (Glover et al., 2015), the sexual reproduction of this species is still unclear and may help to understand the Lgh population dynamics.

Portillo Lemus et al. (2021) showed that two floral morphs of Lgh co-exist in natura, involving self-compatibility for short-styled phenotype and self-incompatibility for long-styled phenotype processes. This new article (Portillo Lemus et al., 2022) goes further and details the underlying mechanisms of the sexual reproduction of the two floral morphs.

Complementing their previous study, the authors have described a late self-incompatible process associated with the long-styled morph, which authorized a small proportion of autogamy. Although this represents a small fraction of the L-morph reproduction, it may have a considerable impact on the L-morph population dynamics. Indeed, authors report that “floral morphs are mostly found in allopatric monomorphic populations (i.e., exclusively S-morph or exclusively L-morph populations)” with a large proportion of L-morph populations compared to S-morph populations in the field. It may seem counterintuitive as L-morph mainly relies on cross-fecundation. 

Results show that L-morph autogamy mainly occurs in the fall, late in the reproduction season. Therefore, the reproduction may be ensured if no exogenous pollen reaches the stigma of L-morph individuals. It partly explains the large proportion of L-morph populations in the field. 

Beyond the description of late-acting self-incompatibility, which makes the Onagraceae a third family of Myrtales with this reproductive adaptation, the study raises several ecological questions linked to the results presented in the article. First, it seems that even if autogamy is possible, Lgh would favour allogamy, even in S-morph, through the faster development of pollen tubes from other individuals. This may confer an adaptative and evolutive advantage for the Lgh, increasing its invasive potential. The article shows this faster pollen tube development in S-morph but does not test the evolutive consequences. It is an interesting perspective for future research. It would also be interesting to describe cellular processes which recognize and then influence the speed of the pollen tube. Second, the importance of sexual reproduction vs vegetative reproduction would also provide information on the benefits of sexual dimorphism within populations. For instance, how fruit production increases the dispersal potential of Lgh would help to understand Lgh population dynamics and to propose adapted management practices (Delbart et al., 2013; Meisler, 2009).

To conclude, the study proposes a morphological, reproductive and physiological description of the Lgh sexual reproduction process. However, underlying ecological questions are well included in the article and the ecophysiological results enlighten some questions about the role of sexual reproduction in the invasiveness of Lgh. I advise the reader to pay attention to the reviewers’ comments; the debates were very constructive and, thanks to the great collaboration with the authorship, lead to an interesting paper about Lgh reproduction and with promising perspectives in ecology and invasion ecology.

References

Dandelot S, Robles C, Pech N, Cazaubon A, Verlaque R (2008) Allelopathic potential of two invasive alien Ludwigia spp. Aquatic Botany, 88, 311–316. https://doi.org/10.1016/j.aquabot.2007.12.004

Delbart E, Mahy G, Monty A (2013) Efficacité des méthodes de lutte contre le développement de cinq espèces de plantes invasives amphibies : Crassula helmsii, Hydrocotyle ranunculoides, Ludwigia grandiflora, Ludwigia peploides et Myriophyllum aquaticum (synthèse bibliographique). BASE, 17, 87–102. https://popups.uliege.be/1780-4507/index.php?id=9586

Gérard J, Brion N, Triest L (2014) Effect of water column phosphorus reduction on competitive outcome and traits of Ludwigia grandiflora and L. peploides, invasive species in Europe. Aquatic Invasions, 9, 157–166. https://doi.org/10.3391/ai.2014.9.2.04

Glover R, Drenovsky RE, Futrell CJ, Grewell BJ (2015) Clonal integration in Ludwigia hexapetala under different light regimes. Aquatic Botany, 122, 40–46. https://doi.org/10.1016/j.aquabot.2015.01.004

Hieda S, Kaneko Y, Nakagawa M, Noma N (2020) Ludwigia grandiflora (Michx.) Greuter & Burdet subsp. hexapetala (Hook. & Arn.) G. L. Nesom & Kartesz, an Invasive Aquatic Plant in Lake Biwa, the Largest Lake in Japan. Acta Phytotaxonomica et Geobotanica, 71, 65–71. https://doi.org/10.18942/apg.201911

Meisler J (2009) Controlling Ludwigia hexaplata in Northern California. Wetland Science and Practice, 26, 15–19. https://doi.org/10.1672/055.026.0404

Portillo Lemus LO, Harang M, Bozec M, Haury J, Stoeckel S, Barloy D (2022) Late-acting self-incompatible system, preferential allogamy and delayed selfing in the heteromorphic invasive populations of Ludwigia grandiflora subsp. hexapetala. bioRxiv, 2021.07.15.452457, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2021.07.15.452457

Portillo Lemus LO, Bozec M, Harang M, Coudreuse J, Haury J, Stoeckel S, Barloy D (2021) Self-incompatibility limits sexual reproduction rather than environmental conditions in an invasive water primrose. Plant-Environment Interactions, 2, 74–86. https://doi.org/10.1002/pei3.10042

Reid AJ, Carlson AK, Creed IF, Eliason EJ, Gell PA, Johnson PTJ, Kidd KA, MacCormack TJ, Olden JD, Ormerod SJ, Smol JP, Taylor WW, Tockner K, Vermaire JC, Dudgeon D, Cooke SJ (2019) Emerging threats and persistent conservation challenges for freshwater biodiversity. Biological Reviews, 94, 849–873. https://doi.org/10.1111/brv.12480

Thouvenot L, Haury J, Thiebaut G (2013) A success story: water primroses, aquatic plant pests. Aquatic Conservation: Marine and Freshwater Ecosystems, 23, 790–803. https://doi.org/10.1002/aqc.2387

Thouvenot L, Haury J, Thiébaut G (2013) Seasonal plasticity of Ludwigia grandiflora under light and water depth gradients: An outdoor mesocosm experiment. Flora - Morphology, Distribution, Functional Ecology of Plants, 208, 430–437. https://doi.org/10.1016/j.flora.2013.07.004